Feature Extraction for Emotion Recognition in Speech with Machine Learning Algorithm

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

Journal of Information System and Technology Management ◽

10.35631/jistm.622005 ◽

2021 ◽

Vol 6 (22) ◽

pp. 51-59

Author(s):

Mustazzihim Suhaidi ◽

Rabiah Abdul Kadir ◽

Sabrina Tiun

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Feature Selection ◽

Input Data ◽

Feature Vector ◽

Learning Algorithm ◽

Extraction Methods ◽

Machine Learning Algorithm ◽

Learning Tasks ◽

Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

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An Analysis of Machine Learning Algorithm for the Classification of Emotion Recognition

10.1007/978-981-16-2712-5_33 ◽

2021 ◽

pp. 399-408

Author(s):

Aditi Sakalle ◽

Pradeep Tomar ◽

Harshit Bhardwaj ◽

Divya Acharya ◽

Arpit Bhardwaj

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Learning Algorithm ◽

Machine Learning Algorithm

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Emotion Recognizing Virtual Assistant

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35151 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1554-1559

Author(s):

Prof. Y. D. Choudhari

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Learning Algorithm ◽

Task Completion ◽

Machine Learning Algorithm ◽

Python Language ◽

Effective Product

Virtual Assistants are the most effective product of AI which makes people’s life easier. They are used in many machines. With AI many other technologies are also born like emotion recognition. This paper presents the AI with the feature of emotion recognition. This AI will complete the task by considering the emotion of user. As so as it takes the command it will analyze the task but before it perform it will recognize the emotion of user and then according to it, it will proceed for task completion. We have used Python language with machine learning algorithm. It is very effective to detect the emotions and avoid any problems. It will provide closer interaction with user like friend.

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Optimization of machine learning algorithm of emotion recognition in terms of human facial expressions

Procedia Computer Science ◽

10.1016/j.procs.2020.02.143 ◽

2020 ◽

Vol 169 ◽

pp. 244-248 ◽

Cited By ~ 1

Author(s):

Ekaterina Ivanova ◽

Georgii Borzunov

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Facial Expressions ◽

Learning Algorithm ◽

Machine Learning Algorithm

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Feature Engineering for Various Data Types in Data Science

Advances in Data Mining and Database Management - Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science ◽

10.4018/978-1-7998-6659-6.ch001 ◽

2021 ◽

pp. 1-16

Author(s):

Nilesh Kumar Sahu ◽

Manorama Patnaik ◽

Itu Snigdh

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Input Data ◽

Data Science ◽

Learning Algorithm ◽

Feature Engineering ◽

Machine Learning Algorithm ◽

Data Types ◽

Data Set ◽

Machine Learning Models

The precision of any machine learning algorithm depends on the data set, its suitability, and its volume. Therefore, data and its characteristics have currently become the predominant components of any predictive or precision-based domain like machine learning. Feature engineering refers to the process of changing and preparing this input data so that it is ready for training machine learning models. Several features such as categorical, numerical, mixed, date, and time are to be considered for feature extraction in feature engineering. Datasets containing characteristics such as cardinality, missing data, and rare labels for categorical features, distribution, outliers, and magnitude are currently considered as features. This chapter discusses various data types and their techniques for applying to feature engineering. This chapter also focuses on the implementation of various data techniques for feature extraction.

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Aspect based feature extraction and sentiment classification of review data sets using Incremental machine learning algorithm

2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB) ◽

10.1109/aeeicb.2017.7972395 ◽

2017 ◽

Cited By ~ 4

Author(s):

Rajalaxmi Hegde ◽

Seema S.

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Algorithm ◽

Sentiment Classification ◽

Data Sets ◽

Machine Learning Algorithm

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Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

Innovation in Aging ◽

10.1093/geroni/igaa057.2262 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 656-656

Author(s):

Youngjun Kim ◽

Uchechukuwu David ◽

Yeonsik Noh

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Classification Accuracy ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Intrinsic Mode Functions ◽

Time Frequency ◽

Mode Decomposition ◽

Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

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